TY - BOOK ID - 134541433 TI - Advances in Quantitative Remote Sensing in China - In Memory of Prof. Xiaowen Li AU - Shi, Jiancheng AU - Liang, Shunlin AU - Yan, Guangjian PY - 2019 PB - MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - gross primary production (GPP) KW - interference filter KW - Visible Infrared Imaging Radiometer Suite (VIIRS) KW - cost-efficient KW - precipitation KW - topographic effects KW - land surface temperature KW - Land surface emissivity KW - scale effects KW - spatial-temporal variations KW - statistics methods KW - inter-annual variation KW - spatial representativeness KW - FY-3C/MERSI KW - sunphotometer KW - PROSPECT KW - passive microwave KW - flux measurements KW - urban scale KW - vegetation dust-retention KW - multiple ecological factors KW - leaf age KW - standard error of the mean KW - LUT method KW - spectra KW - SURFRAD KW - Land surface temperature KW - aboveground biomass KW - uncertainty KW - land surface variables KW - copper KW - Northeast China KW - forest disturbance KW - end of growing season (EOS) KW - random forest model KW - probability density function KW - downward shortwave radiation KW - machine learning KW - MODIS products KW - composite slope KW - daily average value KW - canopy reflectance KW - spatiotemporal representative KW - light use efficiency KW - hybrid method KW - disturbance index KW - quantitative remote sensing inversion KW - SCOPE KW - GPP KW - South China’s KW - anisotropic reflectance KW - vertical structure KW - snow cover KW - land cover change KW - start of growing season (SOS) KW - MS–PT algorithm KW - aerosol KW - pixel unmixing KW - HiWATER KW - algorithmic assessment KW - surface radiation budget KW - latitudinal pattern KW - ICESat GLAS KW - vegetation phenology KW - SIF KW - metric comparison KW - Antarctica KW - spatial heterogeneity KW - comprehensive field experiment KW - reflectance model KW - sinusoidal method KW - NDVI KW - BRDF KW - cloud fraction KW - NPP KW - VPM KW - China KW - dense forest KW - vegetation remote sensing KW - <i>Cunninghamia</i> KW - high resolution KW - geometric-optical model KW - phenology KW - LiDAR KW - ZY-3 MUX KW - point cloud KW - multi-scale validation KW - Fraunhofer Line Discrimination (FLD) KW - rice KW - fractional vegetation cover (FVC) KW - interpolation KW - high-resolution freeze/thaw KW - drought KW - Synthetic Aperture Radar (SAR) KW - controlling factors KW - sampling design KW - downscaling KW - n/a KW - Chinese fir KW - MRT-based model KW - RADARSAT-2 KW - northern China KW - leaf area density KW - potential evapotranspiration KW - black-sky albedo (BSA) KW - decision tree KW - CMA KW - fluorescence quantum efficiency in dark-adapted conditions (FQE) KW - surface solar irradiance KW - validation KW - geographical detector model KW - vertical vegetation stratification KW - spatiotemporal distribution and variation KW - gap fraction KW - phenological parameters KW - spatio-temporal KW - albedometer KW - variability KW - GLASS KW - gross primary productivity (GPP) KW - EVI2 KW - machine learning algorithms KW - latent heat KW - GLASS LAI time series KW - boreal forest KW - leaf KW - maize KW - heterogeneity KW - temperature profiles KW - crop-growing regions KW - satellite observations KW - rugged terrain KW - species richness KW - voxel KW - LAI KW - TMI data KW - GF-1 WFV KW - spectral KW - HJ-1 CCD KW - leaf area index KW - evapotranspiration KW - land-surface temperature products (LSTs) KW - SPI KW - AVHRR KW - Tibetan Plateau KW - snow-free albedo KW - PROSPECT-5B+SAILH (PROSAIL) model KW - MCD43A3 C6 KW - 3D reconstruction KW - photoelectric detector KW - multi-data set KW - BEPS KW - aerosol retrieval KW - plant functional type KW - multisource data fusion KW - remote sensing KW - leaf spectral properties KW - solo slope KW - land surface albedo KW - longwave upwelling radiation (LWUP) KW - terrestrial LiDAR KW - AMSR2 KW - geometric optical radiative transfer (GORT) model KW - MuSyQ-GPP algorithm KW - tree canopy KW - FY-3C/MWRI KW - meteorological factors KW - solar-induced chlorophyll fluorescence KW - metric integration KW - observations KW - polar orbiting satellite KW - arid/semiarid KW - homogeneous and pure pixel filter KW - thermal radiation directionality KW - biodiversity KW - gradient boosting regression tree KW - forest canopy height KW - Landsat KW - subpixel information KW - MODIS KW - humidity profiles KW - NIR KW - geostationary satellite KW - South China's KW - MS-PT algorithm UR - https://www.unicat.be/uniCat?func=search&query=sysid:134541433 AB - Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing. ER -